Collective behaviour refers to coordinated group motion, common to many animals.
The dynamics of a group can be seen as a distributed model, each animal
applying the same rule set. This study investigates the use of an evolved
controller to produce schooling behaviour. A set of artificial creatures
'live' in an artificial world with hazards and food. Each creature has
an artificial neural network brain that controls movement in different
situations. A chromosome encodes the network structure, which may be combined
using artificial evolution with another chromosome, if a creature should
choose to mate. Prey and predators coevolve to produce sophisticated,
non-deterministic, behaviour. Particularly the work highlights the consideration
for understanding the physiology of the species to understanding behaviour.
For example, we found that prey require low-resolution visual systems
to provide a global outlook whereas predators prefer dominant frontal
vision. These and many other interesting conclusions are discussed.This
webpage describes the current state of the project.

Background Information

Evolution is marked by developments of behavioural and physical characteristics.
Yet some behaviours appear innate; collective behaviours such as conformity,
obedience and leadership have been studied for years in social psychology.
Yet we can see primitive characteristics of these behaviours in animals,
such as fish schools, insect swarms and bird flocks. Even across such
a diverse set of creatures, the dynamics are very similar. Partridge
[19] states that collective behaviour occurs when animals "move
in unison, more as a single organism than a collection of individuals".
Movement is dependent on the characteristics of the animal; for example
insects can fly in 3 dimensions, unlike sheep which are restricted to
2 dimensions. The circumstances that stimulate movement differ too,
for example the presence of prey or cold climates. This paper considers
whether sensor configuration is also important to collective behaviour.
From an evolutionary standpoint it is understandable why collective
behaviours are prevalent in such a range of creatures. Biologists propose
several hypotheses for flocking behaviour. It serves to reduce the risk
of being eaten by a predator, provides mating efficiency, enables finding
food easier and is a good environment for learning and reducing overall
aggression [24]. It may also save energy through reducing drag [18].
Zoologists and other scientists have studied collective behaviour in
nature for a long time, but these phenomena have proven to be very difficult
to study objectively without threatening ecological validity. Partridge
[19] for example used a circular tank 10 meters in diameter with a central
gantry; experimenters projected a light over the fish, which were conditioned
to be attracted to it. Fish were marked with a number and, as the gantry
moved, the fish were tracked by measuring various distances to investigate
the adjustment of position within the school.
In recent years, computer modelling and simulation has provided a concrete
way to test and derive new theories. Reynolds [21] presented the seminal
Artificial Life (ALIFE) work in collective behaviour. His program BOIDS,
which implemented artificial birds, did not make any pretence that it
represented the behaviour of birds. Instead, its objective was to produce
convincing flocking behaviour. Each BOID executed three simple rules
or tendencies in the presence of neighbours. Using these three simple
rules, complex global behaviour emerged from simple local interactions.
The results have been reproduced many times. Some variation on the initial
rule set and the method of obtaining neighbours has been explored. In
general, changing depth of vision and parameters such as tendency to
change velocity and heading in response to neighbours, can drastically
alter the structure of the flock.
Since Reynolds [21] little research has been carried out to devise a
rule set that can produce more realistic behaviour without compromising
the sheer simplicity of the original work. Mataric [14] successfully
developed robots to produce flocking behaviour. Mataric states that
collective behaviour is the weighted combination of a number of basic
interactions: collision avoidance, following, dispersion, aggregation
and homing. By programming each of these behaviours into several robots
and then setting a weight that determined which was more likely to execute,
Mataric was able to produce some fairly sophisticated collective behaviour.
These 'behavioural' models are suitable for defining what characteristics
to look for when identifying collective behaviour. But they were 'hand
written'. Rather than consider the environment and physiology of the
species, they are based on some concept about what principles might
be considered important [22]. By focusing on behaviour alone, these
models create a deterministic, 1 dimensional controller. Knowledge is
generally represented in productions (i.e. 'if then ' rules)
and these models frequently neglect sensory modalities.

The system is a combination of VRML and C++. The main program, written
in C++, controls the population dynamics such as evolution, movement,
and the ecology for each generation. The output depicting the movements
can be viewed using a separate system. The population dynamics are displayed
via VRML source code generated by the main program, which is parsed
by an external VRML browser. This gives complete control over the viewing
angle, speed, blurring, zooming and colouring and enables dynamic navigation
in the environment, all at the touch of a button. VRML is a platform
independent language, and one of the unique features of this system
is that YOU can judge the results for yourself.

Listed below are a selection of behavioural dynamics observed, by following
the link you may view the motion..

VRML performance is dependent on the speed of your PC and the amount
of RAM it has. It will also benefit from an accelerated graphics card
(provided the card supports windows acceleration). More VRML browsers
and resources can be found at www.vrml.org.

Disclaimer - I have made every effort to ensure that these files
are safe but I will not be held responsible for any damage these files
do to your computer.

Bremermann, H.J. (1958) The Evolution of Intelligence. The Nervous
System as a Model of its Environment. Technical Report No. 1, Contract
No. 477(17), Dept. of Mathematics, Univ. of Washington, Seattle.

DeJong K.A. (1975) Analysis of behaviour of a class of genetic
adaptive systems PhD Thesis, University of Michigan, Dept. Computer
and Communication Sciences.

Mataric M.J. (1992) Designing Emergent Behaviours: From Local Interations
to Collective Intelligence. In From Animals to Animats 2: Proceedings
of the Second International Conference on Simulation of Adaptive Behaviour,
Cambridge, MA: MIT Press, pp.432-441.

Reynolds C.W. (1992) An Evolved, Vision-Based Behavioural Model
of Coordinated Group Motion. In From Animals to Animats 2: Proceedings
of the Second International Conference on Simulation of Adaptive Behaviour,
Cambridge, MA: MIT Press, pp.384-392.

Werner G.M. & Dyer M.G. (1992) Evolution of Herding Behaviour in
Artificial Animals. In From Animals to Animats 2: Proceedings of the
Second International Conference on Simulation of Adaptive Behaviour,
Cambridge, MA: MIT Press, pp.393-399